Despite the progress, it remains challenging to build neural fields that represent an entire category of objects.
Previous methods sidestep the problem by overfitting on a single instance [1]}, or learning [2]}, [3]}, [4]} on datasets like ShapeNet [5]} that contain objects that are manually canonicalized – oriented consistently for 3D position and orientation (3D pose) across a category.
This strong supervision makes it easier to learn over categories,
but limits their application to data that contain these labels.
Recent work has proposed methods for self-supervised learning of 3D pose canonicalization [6]}, [7]}, [8]}, however, these operate on 3D point clouds, meshes or voxels – but not neural fields.
